AI-Driven Cybercrime Forensics for Predictive Threat Detection and Investigative Intelligence

Abstract

AI-Driven Cybercrime Forensics has become increasingly important for predictive threat detection and investigative intelligence due to the rising complexity and volume of digital evidence. This quantitative study examined how AI-driven forensic capabilities predicted key investigative outcomes, including investigative efficiency, decision accuracy, and case documentation quality. Data were obtained from 210 valid respondents working in cybercrime-related roles. The sample included digital forensics analysts (24.8%), incident response specialists (21.0%), SOC analysts or threat hunters (18.1%), and cybersecurity managers (13.3%). Most respondents reported high familiarity with AI-based security tools (43.8%) and high exposure to multi-source forensic evidence (45.7%). Descriptive results showed high agreement for predictive threat detection effectiveness (M = 4.12, SD = 0.61) and investigative intelligence quality (M = 4.05, SD = 0.64). Workflow efficiency also scored strongly (M = 3.98, SD = 0.69). Explain ability and trust calibration produced the lowest mean (M = 3.74, SD = 0.78), while evidence traceability and documentation integrity remained moderate-high (M = 3.81, SD = 0.73). Reliability analysis confirmed acceptable-to-strong internal consistency, with Cronbach’s alpha values ranging from 0.78 to 0.89. Multiple regression results indicated that investigative intelligence quality was the strongest predictor of investigative efficiency (β = .41, t = 7.82, p < .001), decision accuracy (β = .34, t = 6.11, p < .001), and documentation quality (β = .29, t = 5.02, p < .001). Workflow efficiency significantly predicted investigative efficiency (β = .33, p = .001) and decision accuracy (β = .21, p = .018). The regression models explained 62% of the variance in investigative efficiency (R² = .62), 55% in decision accuracy (R² = .55), and 49% in documentation quality (R² = .49). Overall, findings confirmed that AI-supported correlation and intelligence generation most strongly improved investigative outcomes.